






'''simple docstring'''




import pickle

import numpy as np
from matplotlib import pyplot as plt

class   _UpperCAmelCase     :




   """simple docstring"""

   def __init__(						self					:      Union[str, Any] , __UpperCAmelCase					:      List[Any] , __UpperCAmelCase					:      List[str] , __UpperCAmelCase					:      str , __UpperCAmelCase					:      Optional[int] , __UpperCAmelCase					:      Dict , __UpperCAmelCase					:      str=0.2 , __UpperCAmelCase					:      str=0.2						):





         '''simple docstring'''





         _A     	=      bp_numa
         _A     	=      bp_numa
         _A     	=      bp_numa
         _A     	=      conva_get[:2]
         _A     	=      conva_get[2]
         _A     	=      size_pa
         _A     	=      rate_w
         _A     	=      rate_t
         _A     	=      [
             np.mat(-1 * np.random.rand(self.conva[0] , self.conva[0]						) + 0.5						)
             for i in range(self.conva[1]						)
         ]
         _A     	=      np.mat(-1 * np.random.rand(self.num_bpa , self.num_bpa						) + 0.5						)
         _A     	=      np.mat(-1 * np.random.rand(self.num_bpa , self.num_bpa						) + 0.5						)
         _A     	=      -2 * np.random.rand(self.conva[1]						) + 1
         _A     	=      -2 * np.random.rand(self.num_bpa						) + 1
         _A     	=      -2 * np.random.rand(self.num_bpa						) + 1

   def 		lowerCAmelCase					(						self					:      List[Any] , __UpperCAmelCase					:      int						):





         '''simple docstring'''





         _A     	=      {
             "num_bp1": self.num_bpa,
             "num_bp2": self.num_bpa,
             "num_bp3": self.num_bpa,
             "conv1": self.conva,
             "step_conv1": self.step_conva,
             "size_pooling1": self.size_poolinga,
             "rate_weight": self.rate_weight,
             "rate_thre": self.rate_thre,
             "w_conv1": self.w_conva,
             "wkj": self.wkj,
             "vji": self.vji,
             "thre_conv1": self.thre_conva,
             "thre_bp2": self.thre_bpa,
             "thre_bp3": self.thre_bpa,
         }
         with open(__UpperCAmelCase , "wb"						) as f:
               pickle.dump(__UpperCAmelCase , __UpperCAmelCase						)

         print(f'''Model saved： {save_path}'''						)

   @classmethod
   def 		lowerCAmelCase					(						cls					:      List[str] , __UpperCAmelCase					:      Union[str, Any]						):





         '''simple docstring'''





         with open(__UpperCAmelCase , "rb"						) as f:
               _A     	=      pickle.load(__UpperCAmelCase						)  # noqa: S301

         _A     	=      model_dic.get("conv1"						)
         conv_get.append(model_dic.get("step_conv1"						)						)
         _A     	=      model_dic.get("size_pooling1"						)
         _A     	=      model_dic.get("num_bp1"						)
         _A     	=      model_dic.get("num_bp2"						)
         _A     	=      model_dic.get("num_bp3"						)
         _A     	=      model_dic.get("rate_weight"						)
         _A     	=      model_dic.get("rate_thre"						)
         # create model instance
         _A     	=      CNN(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase						)
         # modify model parameter
         _A     	=      model_dic.get("w_conv1"						)
         _A     	=      model_dic.get("wkj"						)
         _A     	=      model_dic.get("vji"						)
         _A     	=      model_dic.get("thre_conv1"						)
         _A     	=      model_dic.get("thre_bp2"						)
         _A     	=      model_dic.get("thre_bp3"						)
         return conv_ins

   def 		lowerCAmelCase					(						self					:      Optional[Any] , __UpperCAmelCase					:      Union[str, Any]						):





         '''simple docstring'''





         return 1 / (1 + np.exp(-1 * x						))

   def 		lowerCAmelCase					(						self					:      List[str] , __UpperCAmelCase					:      Tuple						):





         '''simple docstring'''





         return round(__UpperCAmelCase , 3						)

   def 		lowerCAmelCase					(						self					:      Tuple , __UpperCAmelCase					:      Any , __UpperCAmelCase					:      Any , __UpperCAmelCase					:      Dict , __UpperCAmelCase					:      List[Any] , __UpperCAmelCase					:      int						):





         '''simple docstring'''





         _A     	=      convs[0]
         _A     	=      convs[1]
         _A     	=      np.shape(__UpperCAmelCase						)[0]
         # get the data slice of original image data, data_focus
         _A     	=      []
         for i_focus in range(0 , size_data - size_conv + 1 , __UpperCAmelCase						):
               for j_focus in range(0 , size_data - size_conv + 1 , __UpperCAmelCase						):
                     _A     	=      data[
                         i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
                     ]
                     data_focus.append(__UpperCAmelCase						)
        # calculate the feature map of every single kernel, and saved as list of matrix
         _A     	=      []
         _A     	=      int((size_data - size_conv) / conv_step + 1						)
         for i_map in range(__UpperCAmelCase						):
               _A     	=      []
               for i_focus in range(len(__UpperCAmelCase						)						):
                     _A     	=      (
                         np.sum(np.multiply(data_focus[i_focus] , w_convs[i_map]						)						)
                         - thre_convs[i_map]
                     )
                     featuremap.append(self.sig(__UpperCAmelCase						)						)
               _A     	=      np.asmatrix(__UpperCAmelCase						).reshape(
                   __UpperCAmelCase , __UpperCAmelCase						)
               data_featuremap.append(__UpperCAmelCase						)

         # expanding the data slice to One dimenssion
         _A     	=      []
         for each_focus in data_focus:
               focusa_list.extend(self.Expand_Mat(__UpperCAmelCase						)						)
         _A     	=      np.asarray(__UpperCAmelCase						)
         return focus_list, data_featuremap

   def 		lowerCAmelCase					(						self					:      int , __UpperCAmelCase					:      int , __UpperCAmelCase					:      List[Any] , __UpperCAmelCase					:      Optional[Any]="average_pool"						):





         '''simple docstring'''





         _A     	=      len(featuremaps[0]						)
         _A     	=      int(size_map / size_pooling						)
         _A     	=      []
         for i_map in range(len(__UpperCAmelCase						)						):
               _A     	=      featuremaps[i_map]
               _A     	=      []
               for i_focus in range(0 , __UpperCAmelCase , __UpperCAmelCase						):
                     for j_focus in range(0 , __UpperCAmelCase , __UpperCAmelCase						):
                           _A     	=      feature_map[
                               i_focus : i_focus + size_pooling,
                               j_focus : j_focus + size_pooling,
                           ]
                           if pooling_type == "average_pool":
                                 # average pooling
                                 map_pooled.append(np.average(__UpperCAmelCase						)						)
                           elif pooling_type == "max_pooling":
                                 # max pooling
                                 map_pooled.append(np.max(__UpperCAmelCase						)						)
               _A     	=      np.asmatrix(__UpperCAmelCase						).reshape(__UpperCAmelCase , __UpperCAmelCase						)
               featuremap_pooled.append(__UpperCAmelCase						)
         return featuremap_pooled

   def 		lowerCAmelCase					(						self					:      Tuple , __UpperCAmelCase					:      List[str]						):





         '''simple docstring'''





         _A     	=      []
         for i in range(len(__UpperCAmelCase						)						):
               _A     	=      np.shape(data[i]						)
               _A     	=      data[i].reshape(1 , shapes[0] * shapes[1]						)
               _A     	=      data_listed.getA().tolist()[0]
               data_expanded.extend(__UpperCAmelCase						)
         _A     	=      np.asarray(__UpperCAmelCase						)
         return data_expanded

   def 		lowerCAmelCase					(						self					:      int , __UpperCAmelCase					:      Union[str, Any]						):





         '''simple docstring'''





         _A     	=      np.asarray(__UpperCAmelCase						)
         _A     	=      np.shape(__UpperCAmelCase						)
         _A     	=      data_mat.reshape(1 , shapes[0] * shapes[1]						)
         return data_expanded

   def 		lowerCAmelCase					(						self					:      List[str] , __UpperCAmelCase					:      List[str] , __UpperCAmelCase					:      str , __UpperCAmelCase					:      List[Any] , __UpperCAmelCase					:      Union[str, Any] , __UpperCAmelCase					:      List[Any]						):





         '''simple docstring'''





         _A     	=      []
         _A     	=      0
         for i_map in range(__UpperCAmelCase						):
               _A     	=      np.ones((size_map, size_map)						)
               for i in range(0 , __UpperCAmelCase , __UpperCAmelCase						):
                     for j in range(0 , __UpperCAmelCase , __UpperCAmelCase						):
                           _A     	=      pd_pool[
                               i_pool
                           ]
                           _A     	=      i_pool + 1
               _A     	=      np.multiply(
                   __UpperCAmelCase , np.multiply(out_map[i_map] , (1 - out_map[i_map])						)						)
               pd_all.append(__UpperCAmelCase						)
         return pd_all

   def 		lowerCAmelCase					(						self					:      int , __UpperCAmelCase					:      Tuple , __UpperCAmelCase					:      Optional[int] , __UpperCAmelCase					:      Optional[int] , __UpperCAmelCase					:      int , __UpperCAmelCase					:      Dict , __UpperCAmelCase					:      Dict=bool						):





         '''simple docstring'''





         print("----------------------Start Training-------------------------"						)
         print((" - - Shape: Train_Data  ", np.shape(__UpperCAmelCase						))						)
         print((" - - Shape: Teach_Data  ", np.shape(__UpperCAmelCase						))						)
         _A     	=      0
         _A     	=      []
         _A     	=      10000
         while rp < n_repeat and mse >= error_accuracy:
               _A     	=      0
               print(f'''-------------Learning Time {rp}--------------'''						)
               for p in range(len(__UpperCAmelCase						)						):
                     # print('------------Learning Image: %d--------------'%p)
                     _A     	=      np.asmatrix(datas_train[p]						)
                     _A     	=      np.asarray(datas_teach[p]						)
                     _A	,      _A     	=      self.convolute(
                         __UpperCAmelCase , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
                     _A     	=      self.pooling(__UpperCAmelCase , self.size_poolinga						)
                     _A     	=      np.shape(__UpperCAmelCase						)
                     _A     	=      self._expand(__UpperCAmelCase						)
                     _A     	=      data_bp_input

                     _A     	=      np.dot(__UpperCAmelCase , self.vji.T						) - self.thre_bpa
                     _A     	=      self.sig(__UpperCAmelCase						)
                     _A     	=      np.dot(__UpperCAmelCase , self.wkj.T						) - self.thre_bpa
                     _A     	=      self.sig(__UpperCAmelCase						)

                     # --------------Model Leaning ------------------------
                     # calculate error and gradient---------------
                     _A     	=      np.multiply(
                         (data_teach - bp_outa) , np.multiply(__UpperCAmelCase , (1 - bp_outa)						)						)
                     _A     	=      np.multiply(
                         np.dot(__UpperCAmelCase , self.wkj						) , np.multiply(__UpperCAmelCase , (1 - bp_outa)						)						)
                     _A     	=      np.dot(__UpperCAmelCase , self.vji						)

                     _A     	=      pd_i_all / (self.size_poolinga * self.size_poolinga)
                     _A     	=      pd_conva_pooled.T.getA().tolist()
                     _A     	=      self._calculate_gradient_from_pool(
                         __UpperCAmelCase , __UpperCAmelCase , shape_featuremapa[0] , shape_featuremapa[1] , self.size_poolinga , )
                     # weight and threshold learning process---------
                     # convolution layer
                     for k_conv in range(self.conva[1]						):
                           _A     	=      self._expand_mat(pd_conva_all[k_conv]						)
                           _A     	=      self.rate_weight * np.dot(__UpperCAmelCase , __UpperCAmelCase						)

                           _A     	=      self.w_conva[k_conv] + delta_w.reshape(
                               (self.conva[0], self.conva[0])						)

                           _A     	=      (
                               self.thre_conva[k_conv]
                               - np.sum(pd_conva_all[k_conv]						) * self.rate_thre
                           )
                     # all connected layer
                     _A     	=      self.wkj + pd_k_all.T * bp_outa * self.rate_weight
                     _A     	=      self.vji + pd_j_all.T * bp_outa * self.rate_weight
                     _A     	=      self.thre_bpa - pd_k_all * self.rate_thre
                     _A     	=      self.thre_bpa - pd_j_all * self.rate_thre
                     # calculate the sum error of all single image
                     _A     	=      np.sum(abs(data_teach - bp_outa						)						)
                     error_count += errors
                     # print('   ----Teach      ',data_teach)
                     # print('   ----BP_output  ',bp_out3)
               _A     	=      rp + 1
               _A     	=      error_count / patterns
               all_mse.append(__UpperCAmelCase						)

         def draw_error():
               _A     	=      [error_accuracy for i in range(int(n_repeat * 1.2						)						)]
               plt.plot(__UpperCAmelCase , "+-"						)
               plt.plot(__UpperCAmelCase , "r--"						)
               plt.xlabel("Learning Times"						)
               plt.ylabel("All_mse"						)
               plt.grid(__UpperCAmelCase , alpha=0.5						)
               plt.show()

         print("------------------Training Complished---------------------"						)
         print((" - - Training epoch: ", rp, f'''     - - Mse: {mse:.6f}''')						)
         if draw_e:
               draw_error()
         return mse

   def 		lowerCAmelCase					(						self					:      List[str] , __UpperCAmelCase					:      Any						):





         '''simple docstring'''





         _A     	=      []
         print("-------------------Start Testing-------------------------"						)
         print((" - - Shape: Test_Data  ", np.shape(__UpperCAmelCase						))						)
         for p in range(len(__UpperCAmelCase						)						):
               _A     	=      np.asmatrix(datas_test[p]						)
               _A	,      _A     	=      self.convolute(
                   __UpperCAmelCase , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
               _A     	=      self.pooling(__UpperCAmelCase , self.size_poolinga						)
               _A     	=      self._expand(__UpperCAmelCase						)

               _A     	=      data_bp_input
               _A     	=      bp_outa * self.vji.T - self.thre_bpa
               _A     	=      self.sig(__UpperCAmelCase						)
               _A     	=      bp_outa * self.wkj.T - self.thre_bpa
               _A     	=      self.sig(__UpperCAmelCase						)
               produce_out.extend(bp_outa.getA().tolist()						)
         _A     	=      [list(map(self.do_round , __UpperCAmelCase						)						) for each in produce_out]
         return np.asarray(__UpperCAmelCase						)

   def 		lowerCAmelCase					(						self					:      Dict , __UpperCAmelCase					:      Tuple						):





         '''simple docstring'''





         _A     	=      np.asmatrix(__UpperCAmelCase						)
         _A	,      _A     	=      self.convolute(
             __UpperCAmelCase , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
         _A     	=      self.pooling(__UpperCAmelCase , self.size_poolinga						)

         return data_conveda, data_pooleda


if __name__ == "__main__":
    pass






"""simple docstring"""



import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


SCREAMING_SNAKE_CASE__:List[str]									=			logging.getLogger(__name__)



def 	_lowerCamelCase(	):
       __a       =    argparse.ArgumentParser(
           description="Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset."				)
       parser.add_argument(
           "--dataset_name"   ,    type=a   ,    default="wikitext"   ,    help="Name of the training. Explore datasets at: hf.co/datasets."   ,    )
       parser.add_argument(
           "--dataset_config"   ,    type=a   ,    default="wikitext-103-raw-v1"   ,    help="Configuration name of the dataset."				)
       parser.add_argument(
           "--tokenizer_name_or_path"   ,    type=a   ,    default="sayakpaul/unigram-tokenizer-wikitext"   ,    help="Tokenizer identifier. Can be a local filepath or a Hub identifier."   ,    )
       parser.add_argument(
           "--shard_size"   ,    type=a   ,    default=1_0_0_0   ,    help="Number of entries to go in a single shard."   ,    )
       parser.add_argument("--split"   ,    type=a   ,    default="train"   ,    choices=["train", "test", "validation"]				)
       parser.add_argument(
           "--limit"   ,    default=a   ,    type=a   ,    help="Limit the number of shards (used for debugging)."   ,    )
       parser.add_argument(
           "--max_length"   ,    type=a   ,    default=5_1_2   ,    help="Maximum sequence length. For training on TPUs, it helps to have a maximum"
           " sequence length that is a multiple of 8."   ,    )
       parser.add_argument(
           "--output_dir"   ,    default="tf-tpu"   ,    type=a   ,    help="Output directory where the TFRecord shards will be saved. If the"
           " path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord"
           " shards will be directly saved to a Google Cloud Storage bucket."   ,    )

       __a       =    parser.parse_args()
       return args



def 	_lowerCamelCase(	a				):
       def fn(a				):
              return tokenizer(examples["text"]				)

       return fn



def 	_lowerCamelCase(	a				):
       __a       =    []
       for i in range(len(tokenized_data["input_ids"]				)				):
              __a       =    {
                  "input_ids": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data["input_ids"][i]				)				),
                  "attention_mask": tf.train.Feature(
                      intaa_list=tf.train.IntaaList(value=tokenized_data["attention_mask"][i]				)				),
              }
              __a       =    tf.train.Features(feature=a				)
              __a       =    tf.train.Example(features=a				)
              __a       =    example.SerializeToString()
              records.append(a				)
       return records



def 	_lowerCamelCase(	a				):
       __a       =    datasets.load_dataset(args.dataset_name   ,    args.dataset_config   ,    split=args.split				)

       if args.limit is not None:
              __a       =    min(len(a				)   ,    args.limit				)
              __a       =    dataset.select(range(a				)				)
              print(F"Limiting the dataset to {args.limit} entries."				)

       __a       =    AutoTokenizer.from_pretrained(args.tokenizer_name_or_path				)

       # Handle output directory creation.
       # For serializing into a Google Cloud Storage Bucket, one needs to first
       # create a bucket.
       if "gs" not in args.output_dir:
              if not os.path.exists(args.output_dir				):
                     os.makedirs(args.output_dir				)
              __a       =    os.path.join(args.output_dir   ,    args.split				)
              if not os.path.exists(a				):
                     os.makedirs(a				)
       else:
              __a       =    os.path.join(args.output_dir   ,    args.split				)

       # Tokenize the whole dataset at once.
       __a       =    tokenize_function(a				)
       __a       =    dataset.map(a   ,    batched=a   ,    num_proc=4   ,    remove_columns=["text"]				)

       # We need to concatenate all our texts together, and then split the result
       # into chunks of a fixed size, which we will call block_size. To do this, we
       # will use the map method again, with the option batched=True. When we use batched=True,
       # the function we pass to map() will be passed multiple inputs at once, allowing us
       # to group them into more or fewer examples than we had in the input.
       # This allows us to create our new fixed-length samples. The advantage of this
       # method is that we don't lose a whole lot of content from the dataset compared to the
       # case where we simply tokenize with a pre-defined max_length.

       def group_texts(a				):
              # Concatenate all texts.
              __a       =    {k: sum(examples[k]   ,    []				) for k in examples.keys()}
              __a       =    len(concatenated_examples[list(examples.keys()				)[0]]				)
              # We drop the small remainder, though you could add padding instead if the model supports it
              # In this, as in all things, we advise you to follow your heart 🫀
              __a       =    (total_length // args.max_length) * args.max_length
              # Split by chunks of max_len.
              __a       =    {
                  k: [t[i : i + args.max_length] for i in range(0   ,    a   ,    args.max_length				)]
                  for k, t in concatenated_examples.items()
              }
              return result

       __a       =    dataset_tokenized.map(a   ,    batched=a   ,    batch_size=1_0_0_0   ,    num_proc=4				)

       __a       =    0
       __a       =    0
       for shard in range(0   ,    len(a				)   ,    args.shard_size				):
              __a       =    grouped_dataset[shard : shard + args.shard_size]
              __a       =    len(dataset_snapshot["input_ids"]				)
              __a       =    os.path.join(a   ,    F"dataset-{shard_count}-{records_containing}.tfrecord"				)
              __a       =    get_serialized_examples(a				)

              with tf.io.TFRecordWriter(a				) as out_file:
                     for i in range(len(a				)				):
                            __a       =    serialized_examples[i]
                            out_file.write(a				)
                     print("Wrote file {} containing {} records".format(a   ,    a				)				)

              shard_count += 1
              total_records += records_containing

       with open(F"split-{args.split}-records-count.txt"   ,    "w"				) as f:
              print(F"Total {args.split} records: {total_records}"   ,    file=a				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:List[Any]									=			parse_args()
   main(args)
